Feeds:
Posts
Comments

Archive for the ‘Population Health Management, Genetics & Pharmaceutical’ Category


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

During pregnancy, the baby is mostly protected from harmful microorganisms by the amniotic sac, but recent research suggests the baby could be exposed to small quantities of microbes from the placenta, amniotic fluid, umbilical cord blood and fetal membranes. One theory is that any possible prenatal exposure could ‘pre-seed’ the infant microbiome. In other words, to set the right conditions for the ‘main seeding event’ for founding the infant microbiome.

When a mother gives birth vaginally and if she breastfeeds, she passes on colonies of essential microbes to her baby. This continues a chain of maternal heritage that stretches through female ancestry for thousands of generations, if all have been vaginally born and breastfed. This means a child’s microbiome, that is the trillions of microorganisms that live on and in him or her, will resemble the microbiome of his/her mother, the grandmother, the great-grandmother and so on, if all have been vaginally born and breastfed.

As soon as the mother’s waters break, suddenly the baby is exposed to a wave of the mother’s vaginal microbes that wash over the baby in the birth canal. They coat the baby’s skin, and enter the baby’s eyes, ears, nose and some are swallowed to be sent down into the gut. More microbes form of the mother’s gut microbes join the colonization through contact with the mother’s faecal matter. Many more microbes come from every breath, from every touch including skin-to-skin contact with the mother and of course, from breastfeeding.

With formula feeding, the baby won’t receive the 700 species of microbes found in breast milk. Inside breast milk, there are special sugars called human milk oligosaccharides (HMO’s) that are indigestible by the baby. These sugars are designed to feed the mother’s microbes newly arrived in the baby’s gut. By multiplying quickly, the ‘good’ bacteria crowd out any potentially harmful pathogens. These ‘good’ bacteria help train the baby’s naive immune system, teaching it to identify what is to be tolerated and what is pathogen to be attacked. This leads to the optimal training of the infant immune system resulting in a child’s best possible lifelong health.

With C-section birth and formula feeding, the baby is not likely to acquire the full complement of the mother’s vaginal, gut and breast milk microbes. Therefore, the baby’s microbiome is not likely to closely resemble the mother’s microbiome. A baby born by C-section is likely to have a different microbiome from its mother, its grandmother, its great-grandmother and so on. C-section breaks the chain of maternal heritage and this break can never be restored.

The long term effect of an altered microbiome for a child’s lifelong health is still to be proven, but many studies link C-section with a significantly increased risk for developing asthma, Type 1 diabetes, celiac disease and obesity. Scientists might not yet have all the answers, but the picture that is forming is that C-section and formula feeding could be significantly impacting the health of the next generation. Through the transgenerational aspect to birth, it could even be impacting the health of future generations.

References:

https://blogs.scientificamerican.com/guest-blog/shortchanging-a-babys-microbiome/

https://www.ncbi.nlm.nih.gov/pubmed/23926244

https://www.ncbi.nlm.nih.gov/pubmed/26412384

https://www.ncbi.nlm.nih.gov/pubmed/25290507

https://www.ncbi.nlm.nih.gov/pubmed/25974306

https://www.ncbi.nlm.nih.gov/pubmed/24637604

https://www.ncbi.nlm.nih.gov/pubmed/22911969

https://www.ncbi.nlm.nih.gov/pubmed/25650398

https://www.ncbi.nlm.nih.gov/pubmed/27362264

https://www.ncbi.nlm.nih.gov/pubmed/27306663

http://www.mdpi.com/1099-4300/14/11/2036

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464665/

https://www.ncbi.nlm.nih.gov/pubmed/24848255

https://www.ncbi.nlm.nih.gov/pubmed/26412384

https://www.ncbi.nlm.nih.gov/pubmed/28112736

http://ndnr.com/gastrointestinal/the-infant-microbiome-how-environmental-maternal-factors-influence-its-development/

Advertisements

Read Full Post »


Finding the Actions That Alter Evolution

The biologist Marcus Feldman creates mathematical models that reveal how cultural traditions can affect the evolution of a species.

By Elizabeth Svoboda

January 5, 2017

In a commentary in Nature, you and your co-authors wrote, “We hold that organisms are constructed in development, not simply ‘programmed’ to develop by genes.” What does “constructed in development” mean?

It means there’s an interaction between the subject and the environment. The idea of a genetic blueprint is not tenable in light of all that is now known about how all sorts of environmental contingencies affect traits. For many animals it’s like that. Even plants — the same plant that is genetically identical, if you put it in this environment, it’s going to look totally different from if you put it in that environment.

We now have a better picture of the regulatory process on genes. Epigenetics changes the landscape in genetics because it’s not only the pure DNA sequence which influences what’s going on at the level of proteins and enzymes. There’s this whole other stuff, the other 95 percent of the genome, that acts like rheostats — you slide this thing up and down, you get more or less of this protein. It’s a critical thing in how much of this protein is going to be made. It’s interesting to think about the way in which cultural phenomena, which we used to think were things by themselves, can have this effect on how much messenger RNA is made, and therefore on many aspects of gene regulation.

Article to review and VIEW VIDEO

https://www.quantamagazine.org/20170105-marcus-feldman-interview-culture-and-evolution/

 

ABOUT QUANTA

Quanta Magazine’s mission is to enhance public understanding of research developments in mathematics and the physical and life sciences. Quanta articles do not necessarily represent the views of the Simons Foundation. Learn more

Read Full Post »


The Extension of Biology Through Culture

Reporter: Aviva Lev-Ari, PhD, RN

 

Arnold and Mabel Beckman Center of the

National Academies of Sciences and Engineering

http://www.thebeckmancenter.org/

Distinctive Voices @ The Beckman Center

SOURCE

From: “Distinctive Voices @ The Beckman Center” <voicesatbeckman@nas.ccsend.com> on behalf of “Distinctive Voices @ The Beckman Center” <voicesatbeckman@nas.edu>

Reply-To: <voicesatbeckman@nas.edu>

Date: Wednesday, October 5, 2016 at 10:01 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RSVP NOW for Science Lecture – October 12

beckman4f7f99de-f7fa-43eb-8b4d-8fb02183dbcd

November 16, 2016

Evolution of Biology Through Culture

Andrew Whiten

University of St. Andrews 

 

The Extension of Biology Through Culture

Organized by Marcus Feldman, Francisco J. Ayala, Andrew Whiten and Kevin Laland

 

November 16-17, 2016

 

 

Nov 15   6:30 PM         Speaker Welcome Dinner at hotel (informal – no program)

 

 

Wednesday, November 16

 

7:30 AM         Bus departs Hotel for Beckman Center

 

7:30 AM         Registration and Buffet Breakfast, Beckman Center Dining Room

 

Session I

 

8:30 AM         Welcome Remarks, Marcus Feldman, Stanford University

 

9:00 AM         Evolution and revolution in cetacean vocal culture: lessons from humpback whale song, Ellen Garland, University of St Andrews, UK

 

9:50 AM         Gene-culture coevolution in whales and dolphins, Hal Whitehead, Dalhousie University
Halifax, Nova Scotia, Canada

 

10:40 AM         Break

 

11:00 AM         Cultural legacies: unpacking the inter-generational transmission of information in birds,
Lucy Aplin, University of Oxford, UK

 

11:50 AM         What evolves in the evolution of social learning? A social insect perspective, Elli
Leadbeater, Queen Mary University of London (QMUL)

 

12:40 PM         Buffet Lunch, Beckman Center Dining Room

 

Session II

 

1:50 PM         Can culture re-shape the evolution of learning and how?, Arnon Lotem, Tel Aviv
University

 

2:40 PM         What long term field studies reveal of primate traditions, Susan Perry, University of
California, Los Angeles

 

3:30 PM         Break (set up posters)

 

4:00 PM         Can we identify a primate signature in social learning? Dorothy Fragaszy, University of
Georgia

 

4:50 PM         The evolution of primate intelligence, Kevin Laland, University of St Andrews, UK

 

5:40 PM         Poster Session and Buffet Dinner (Sackler registrants)

 

7:00 PM         Distinctive Voices Public Lecture

                       How animal cultures extend the scope of biology: Tradition and learning from apes to whales to bees, Andrew Whiten, University of St Andrews, UK

 

8:00 PM         Dessert and Coffee with combined audience

 

8:45 PM         Bus departs Beckman Center for Hotel

 

 

Thursday, November 17

 

7:00 AM         Bus departs Fairmont Newport Beach Hotel for Beckman Center

 

7:00 AM         Buffet Breakfast, Beckman Center Dining Room

 

Session III

 

8:00 AM        The role of cultural innovations, learning processes, and ecological dynamics in
shaping Middle Stone Age cultural adaptations
, Francesco d’Errico, University of                                     Bordeaux, France

 

8:50 AM         The ontogenetic foundations of cumulative cultural transmission, Cristine Legare,                        University of Texas, Austin

 

9:40 AM         Break

 

10:00 AM         “I don’t know”: ignorance and question-asking as engines for cognitive development,
Paul Harris, Harvard University

 

10:50 AM         Childhood as simulated annealing: How wide hypothesis exploration in an extended
childhood contributes to cultural learning
, Alison Gopnik, University of California,                                     Berkeley

 

11:40 AM         Buffet Lunch, Beckman Center Dining Room

 

Session IV

 

12:50 PM         How language shapes the nature of cultural inheritance, Susan Gelman, University of Michigan

 

1:40 PM         Big data and prospects for an evolutionary science of human history, Russel Gray, Max
Planck Institute for the Science of Human History in Jena, Germany

 

2:30 PM         Break

 

2:50 PM         Cultural Evolutionary Psychology, Cecilia Heyes, University of Oxford, UK

 

3:40 PM         Ongoing prospects for a unified science of cultural evolution, Alex Mesoudi, University of Exeter, UK

 

4:30 PM        Concluding Remarks, Francisco J. Ayala, University of California, Irvine

 

4:45 PM        Bus departs Beckman Center for Orange County Airport and Hotel

SOURCE

From: Marcus W Feldman <mfeldman@stanford.edu>

Date: Thursday, October 6, 2016 at 12:16 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Fwd: Sackler program for Irvine:11/16-17

Read Full Post »


genomicsinpersonalizedmedicinecovervolumeone

Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

http://www.amazon.com/dp/B018DHBUO6

electronic Table of Contents

Chapter 1

1.1 Advances in the Understanding of the Human Genome The Initiation and Growth of Molecular Biology and Genomics – Part I

1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

1.3 DNA – The Next-Generation Storage Media for Digital Information

1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology

Chapter 2

2.1 2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

2.2 DNA structure and Oligonucleotides

2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

2.4 Genomics and Evolution

2.5 Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Chapter 3

3.1 Big Data in Genomic Medicine

3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB 

3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome

3.4 Metabolite Identification Combining Genetic and Metabolic Information: Genetic Association Links Unknown Metabolites to Functionally Related Genes

3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

3.7 Genetic basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

3.8 MIT Team Researches Regulatory Motifs and Gene Expression of Erythroleukemia (K562) and Liver Carcinoma (HepG2) Cell Lines

Chapter 4

4.1 ENCODE Findings as Consortium

4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases

4.3 Reveals from ENCODE Project will Invite High Synergistic Collaborations to Discover Specific Targets  

4.4 Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence

4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

4.9  “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

4.10  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

Chapter 5

5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 

5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford

5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3

5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research

5.7 Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

6.2 Ubiquinin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

6.3 Mitochondrial Damage and Repair under Oxidative Stress

6.4 Mitochondria: More than just the “Powerhouse of the Cell”

6.5 Mechanism of Variegation in Immutans

6.6 Impact of Evolutionary Selection on Functional Regions: The imprint of Evolutionary Selection on ENCODE Regulatory Elements is Manifested between Species and within Human Populations

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

6.18  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 7

7.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

7.2 Consumer Market for Personal DNA Sequencing: Part 4

7.3 GSK for Personalized Medicine using Cancer Drugs Needs Alacris Systems Biology Model to Determine the In Silico Effect of the Inhibitor in its “Virtual Clinical Trial”

7.4 Drugging the Epigenome

7.5 Nation’s Biobanks: Academic institutions, Research institutes and Hospitals – vary by Collections Size, Types of Specimens and Applications: Regulations are Needed

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

8.3 The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference, 11/28-29, 2012, Harvard Medical School, Boston, MA

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

8.7 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

8.9 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

9.2 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

9.3 Inform Genomics Developing SNP Test to Predict Side Effects, Help MDs Choose among Chemo Regimens

9.4 SNAP: Predict Effect of Non-synonymous Polymorphisms: How Well Genome Interpretation Tools could Translate to the Clinic

9.5  LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I

9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away

9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

 Chapter 10

10.1 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

10.3 Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Chapter 11

11.1 mRNA Interference with Cancer Expression

11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics

11.3 Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.

11.6 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

11.7 When Clinical Application of miRNAs?

11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,

11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953

12.2 Otto Warburg, A Giant of Modern Cellular Biology

12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

12.4 Hypothesis – Following on James Watson

12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

12.6 AKT signaling variable effects

12.7 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

13.2 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

13.3 Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

13.4 Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease

13.6 In focus: Melanoma Genetics

13.7 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

14.4 Sepsis, Multi-organ Dysfunction Syndrome, and Septic Shock: A Conundrum of Signaling Pathways Cascading Out of Control

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

15.2 Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol

15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

15.4 Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes

15.5 BARI 2D Trial Outcomes

15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts

15.7 Obstructive coronary artery disease diagnosed by RNA levels of 23 genes – CardioDx, a Pioneer in the Field of Cardiovascular Genomic  Diagnostics

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

15.9.1 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

15.9.2. Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

15.9.3 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

15.9.4 The Implications of a Newly Discovered CYP2J2 Gene Polymorphism Associated with Coronary Vascular Disease in the Uygur Chinese Population

15.9.5  Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.

15.10 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

18.2 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

18.4 Ustekinumab New Drug Therapy for Cognitive Decline Resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

18.5 Cell Transplantation in Brain Repair

18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

19.3 Commentary on Dr. Baker’s post “Junk DNA Codes for Valuable miRNAs: Non-coding DNA Controls Diabetes”

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

19.5 Secondary Hypertension caused by Aldosterone-producing Adenomas caused by Somatic Mutations in ATP1A1 and ATP2B3 (adrenal cortical; medullary or Organ of Zuckerkandl is pheochromocytoma)

19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

19.9 Whole-genome Sequencing in Probing the Meiotic Recombination and Aneuploidy of Single Sperm Cells

19.10 Reproductive Genetic Testing

Chapter 20

20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

20.7 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

 20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome

Chapter 21

Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era:  Medical Interpretation of the Genomics Frontier – CRISPR – Cas9

Introduction

21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna

21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna

21.1.2 Evaluate your Cas9 gene editing vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

21.1.3 2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

21.1.4  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics  & The Start Up Landscape in BostonGene Editing – New Technology The Missing link for Gene Therapy?

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation

Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*

21.2.3 New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA

21.2.4 Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard

21.3 Patents Awarded and Pending for CRISPR

21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method

21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century

2.4 CRISPR/Cas9 Applications

21.4.1  Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

21.4.6 Level of Comfort with Making Changes to the DNA of an Organism

21.4.7 Who will be the the First to IPO: Novartis bought in to Intellia (UC, Berkeley) as well as Caribou (UC, Berkeley) vs Editas (MIT)??

21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development

Summary

Read Full Post »


cvd-series-a-volume-iii


Series A: e-Books on Cardiovascular Diseases
 

Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC

VOLUME THREE

Etiologies of Cardiovascular Diseases:

Epigenetics, Genetics and Genomics

http://www.amazon.com/dp/B018PNHJ84

 

by  

Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator

and

Aviva Lev-Ari, PhD, RN, Editor and Curator

Introduction to Volume Three 

PART 1
Genomics and Medicine

1.1  Genomics and Medicine: The Physician’s View

1.2  Ribozymes and RNA Machines – Work of Jennifer A. Doudna

1.3  Genomics and Medicine: Contributions of Genetics and Genomics to Cardiovascular Disease Diagnoses

1.4 Genomics Orientations for Individualized Medicine, Volume One

1.4.1 CVD Epidemiology, Ethnic subtypes Classification, and Medication Response Variability: Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study)

1.4.2 What comes after finishing the Euchromatic Sequence of the Human Genome?

1.5  Genomics in Medicine – Establishing a Patient-Centric View of Genomic Data

 

PART 2
Epigenetics – Modifiable Factors Causing Cardiovascular Diseases

2.1 Diseases Etiology

2.1.1 Environmental Contributors Implicated as Causing Cardiovascular Diseases

2.1.2 Diet: Solids, Fluid Intake and Nutraceuticals

2.1.3 Physical Activity and Prevention of Cardiovascular Diseases

2.1.4 Psychological Stress and Mental Health: Risk for Cardiovascular Diseases

2.1.5 Correlation between Cancer and Cardiovascular Diseases

2.1.6 Medical Etiologies for Cardiovascular Diseases: Evidence-based Medicine – Leading DIAGNOSES of Cardiovascular Diseases, Risk Biomarkers and Therapies

2.1.7 Signaling Pathways

2.1.8 Proteomics and Metabolomics

2.1.9 Sleep and Cardiovascular Diseases

2.2 Assessing Cardiovascular Disease with Biomarkers

2.2.1 Issues in Genomics of Cardiovascular Diseases

2.2.2 Endothelium, Angiogenesis, and Disordered Coagulation

2.2.3 Hypertension BioMarkers

2.2.4 Inflammatory, Atherosclerotic and Heart Failure Markers

2.2.5 Myocardial Markers

2.3  Therapeutic Implications: Focus on Ca(2+) signaling, platelets, endothelium

2.3.1 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

2.3.2 EMRE in the Mitochondrial Calcium Uniporter Complex

2.3.3 Platelets in Translational Research ­ 2: Discovery of Potential Anti-platelet Targets

2.3.4 The Final Considerations of the Role of Platelets and Platelet Endothelial Reactions in Atherosclerosis and Novel Treatments

2.3.5 Nitric Oxide Synthase Inhibitors (NOS-I)

2.3.6 Resistance to Receptor of Tyrosine Kinase

2.3.7 Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation

2.3.8 Advanced Topics in Sepsis and the Cardiovascular System at its End Stage

2.4 Comorbidity of Diabetes and Aging

2.4.1 Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients

2.4.2 Pathophysiological Effects of Diabetes on Ischemic-Cardiovascular Disease and on Chronic Obstructive Pulmonary Disease (COPD)

2.4.3 Risks of Hypoglycemia in Diabetics with Chronic Kidney Disease (CKD)

2.4.4  Mitochondrial Mechanisms of Disease in Diabetes Mellitus

2.4.5 Mitochondria: More than just the “powerhouse of the cell”

2.4.6  Pathophysiology of GLP-1 in Type 2 Diabetes

2.4.7 Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets

2.4.8 CaKMII Inhibition in Obese, Diabetic Mice leads to Lower Blood Glucose Levels

2.4.9 Protein Target for Controlling Diabetes, Fractalkine: Mediator cell-to-cell Adhesion though CX3CR1 Receptor, Released from cells Stimulate Insulin Secretion

2.4.10 Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

2.4.11 CABG or PCI: Patients with Diabetes – CABG Rein Supreme

2.4.12 Reversal of Cardiac Mitochondrial Dysfunction

2.4.13  BARI 2D Trial Outcomes

2.4.14 Overview of new strategy for treatment of T2DM: SGLT2 inhibiting oral antidiabetic agents

2.5 Drug Toxicity and Cardiovascular Diseases

2.5.1 Predicting Drug Toxicity for Acute Cardiac Events

2.5.2 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

2.5.3 Decoding myocardial Ca2+ signals across multiple spatial scales: A role for sensitivity analysis

2.5.4. Leveraging Mathematical Models to Understand Population Variability in Response to Cardiac Drugs: Eric Sobie, PhD

2.5.5 Exploiting mathematical models to illuminate electrophysiological variability between individuals.

2.5.6 Clinical Effects and Cardiac Complications of Recreational Drug Use: Blood pressure changes, Myocardial ischemia and infarction, Aortic dissection, Valvular damage, and Endocarditis, Cardiomyopathy, Pulmonary edema and Pulmonary hypertension, Arrhythmias, Pneumothorax and Pneumopericardium

 

2.6 Male and Female Hormonal Replacement Therapy: The Benefits and the Deleterious Effects on Cardiovascular Diseases

2.6.1  Testosterone Therapy for Idiopathic Hypogonadotrophic Hypogonadism has Beneficial and Deleterious Effects on Cardiovascular Risk Factors

2.6.2 Heart Risks and Hormones (HRT) in Menopause: Contradiction or Clarification?

2.6.3 Calcium Dependent NOS Induction by Sex Hormones: Estrogen

2.6.4 Role of Progesterone in Breast Cancer Progression

PART 3
Determinants of Cardiovascular Diseases Genetics, Heredity and Genomics Discoveries

Introduction

3.1 Why cancer cells contain abnormal numbers of chromosomes (Aneuploidy)

3.1.1 Aneuploidy and Carcinogenesis

3.2 Functional Characterization of Cardiovascular Genomics: Disease Case Studies @ 2013 ASHG

3.3 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013

3.3.1: Heredity of Cardiovascular Disorders

3.3.2: Myocardial Damage

3.3.3: Hypertention and Atherosclerosis

3.3.4: Ethnic Variation in Cardiac Structure and Systolic Function

3.3.5: Aging: Heart and Genetics

3.3.6: Genetics of Heart Rhythm

3.3.7: Hyperlipidemia, Hyper Cholesterolemia, Metabolic Syndrome

3.3.8: Stroke and Ischemic Stroke

3.3.9: Genetics and Vascular Pathologies and Platelet Aggregation, Cardiac Troponin T in Serum

3.3.10: Genomics and Valvular Disease

3.4  Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease

PART 4
Individualized Medicine Guided by Genetics and Genomics Discoveries

4.1 Preventive Medicine: Cardiovascular Diseases

4.1.1 Personal Genomics for Preventive Cardiology Randomized Trial Design and Challenges

4.2 Gene-Therapy for Cardiovascular Diseases

4.2.1 Genetic Basis of Cardiomyopathy

4.3 Congenital Heart Disease/Defects

4.4 Cardiac Repair: Regenerative Medicine

4.4.1 A Powerful Tool For Repairing Damaged Hearts

4.4.2 Modified RNA Induces Vascular Regeneration After a Heart

4.5 Pharmacogenomics for Cardiovascular Diseases

4.5.1 Blood Pressure Response to Antihypertensives: Hypertension Susceptibility Loci Study

4.5.2 Statin-Induced Low-Density Lipoprotein Cholesterol Reduction: Genetic Determinants in the Response to Rosuvastatin

4.5.3 SNPs in apoE are found to influence statin response significantly. Less frequent variants in PCSK9 and smaller effect sizes in SNPs in HMGCR

4.5.4 Voltage-Gated Calcium Channel and Pharmacogenetic Association with Adverse Cardiovascular Outcomes: Hypertension Treatment with Verapamil SR (CCB) vs Atenolol (BB) or Trandolapril (ACE)

4.5.5 Response to Rosuvastatin in Patients With Acute Myocardial Infarction: Hepatic Metabolism and Transporter Gene Variants Effect

4.5.6 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

4.5.7 Is Pharmacogenetic-based Dosing of Warfarin Superior for Anticoagulation Control?

Summary & Epilogue to Volume Three

 

 

Read Full Post »


mRNA data survival analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

LPBI

 

SURVIV for survival analysis of mRNA isoform variation

Shihao ShenYuanyuan WangChengyang WangYing Nian Wu & Yi Xing
Nature Communications7,Article number:11548
 Feb 2016      doi:10.1038/ncomms11548

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA1. Over 95% of the multi-exon human genes undergo alternative splicing2, 3, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer4, 5, 6. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis7, 8. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms9. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth10. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis11, 12.

RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation13, 14. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients15. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.

To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival16, 17. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles24, 25. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation14. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data14, 26, 27, 28,29. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.

Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.

SURVIV statistical model

The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In14). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets14, 26, 27, 28, 29. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).

Figure 1: The statistical framework of the SURVIV model.

(a) For each patient k, the patient’s hazard rate λk(t) is associated with the baseline hazard rate λ0(t) and this patient’s exon-inclusion level ψk. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψk is estimated from the read counts for the exon-inclusion isoform ICk and the exon-skipping isoform SCk. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time Sk(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ0(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads ICk are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SCk are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.

Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψk and hazard rate λk(t).

For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ0(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time Sk(t) can be calculated from the patient-specific hazard rate λk(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ0(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.

The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψk of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (ICk) and the exon-skipping isoform (SCk). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework29. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (lI) and the exon-skipping isoform (lS), the exon-inclusion level ψk can be estimated as . Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk=ICk+SCk, we have:

The binomial distribution models the estimation uncertainty of ψk as influenced by the total read count nk, in which the parameter pk represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψk adjusted by a length normalization function f(ψk) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.

Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψk estimated from RNA-seq count data, and the confidence of ψk estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model30 to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H0:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach31. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.

 

Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.

We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.

 

Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).

To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis32 up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis29. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.

SURVIV analysis of TCGA IDC breast cancer data

To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer33, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC33 and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses34 of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).

Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.

IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.

TABLE 1 (not shown)

Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.

Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.

 

Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.

(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.

Network of survival-associated alternative splicing events

…see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.

(ac) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.

…..

Alternative splicing predictors of cancer patient survival

see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index

A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.

Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.

 

Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis7, 8. The widespread use of RNA-seq in cancer transcriptome studies15, 47, 48 has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer18, 21, 49, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.

We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.

Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis37. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways40, 41, 42, 43, 44, 45. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.

We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification25. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing29, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation12. Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.

The SURVIV source code is available for download at https://github.com/Xinglab/SURVIV. SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.

 

Read Full Post »


CMS releases MACRA rule proposal: Will HHS force physicians to drop fee for service for fee for outcome?

Streamlined implementation aims to increase flexibility, decrease reporting burden for physicians

The U.S. Department of Health and Human Services unveiled a proposed ruletackling the initial implementation of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA).

According to an HHS announcement accompanying the rule, the primary aim is to simplify and streamline the existing patchwork of value-based payment models that have increasingly replaced the traditional fee-for-service system via a new framework dubbed the Quality Payment Program. This structure provides doctors with two paths for compliance:

The Centers for Medicare & Medicaid Services expects most providers to opt for the MIPS track initially, according to CMS Acting Principal Deputy Administrator and Chief Medical Officer Patrick Conway, M.D., who spoke on a conference call announcing the rule.

Participation in Advanced Alternative Payment models would exempt doctors from MIPS reporting requirements while also qualifying them for financial bonuses in exchange for taking on the risks related with providing “coordinated, high-quality care,” according to CMS. The agency expects both the number of physicians participating in this track and the number of payment models available to grow over time.

CMS also reports that doctors will have the flexibility to switch among various components of the Quality Payment Program as dictated by the needs of their patients or their practices.

Opinions from around the web

In this video, Gilberg, senior vice president for the Medical Group Management Association’s Government Affairs Office, discusses CMS’ Physician Value-based Payment Modifier. In 2015, Medicare will begin applying the modifier under the physician fee schedule to various providers to show value of care.

“Cost and quality … make up the value equation, in the mind of the payer, in terms of Medicare,” said Gilberg.

In addition to explaining how the modifier works, Gilberg also highlights other quality measures facing providers under the Physician Quality Reporting System and via the EHR Incentive Programs, better known as meaningful use.

View Video at

http://www.physicianspractice.com/mgma14/understanding-medicare-value-based-payment-models

When the Medicare Access and CHIP Reauthorization Act (MACRA) legislation passed in April 2015, everyone cheered the repeal of the Sustainable Growth Rate (SGR) formula for Medicare physician payment. Now, even before the MACRA regulations are even promulgated, it’s time to pay attention because Medicare physician payments in 2019 will be impacted by their performance in 2017, just a year from now.

Other related articles

Read Full Post »

Older Posts »